Recently, research into self-supervised representations for speech [1]}, [2]}, [3]} has made impressive progress, resulting in audio features that are much better than the traditional MFCC features in representing speaker invariant phonetic content. The learned representations typically span a rather short and fixed-length time window (10 or 20ms) and can be fine-tuned for ASR [2]} with few labels.
However, such representations are still not good enough for other downstream tasks that need to handle larger word-like units that typically span larger time windows (100ms-1000ms) and have varying duration depending on speech rate. These tasks include word segmentation [5]}, [6]}
term discovery [7]}, [8]}, [9]}, [10]}, [11]}, [12]} and unsupervised alignment of text and speech [13]}, [14]}
Despite recent research efforts into developing word embeddings also known as speech sequence embeddings (SSE), [15]}, [16]}, [17]}, [18]}, [19]}
their performances are low and difficult to compare for lack of a common evaluation benchmark.
